Wi-Play: Robust Human Activity Recognition for Somatosensory Game Using Wi-Fi Signals

Existing somatosensory games mainly use vision-based methods, which are affected easily by Line-of-Sight (LOS), occlusions, complex background, wearable devices, and so on. To address these limitations, we propose Wi-Play, a robust human activity recognition system using Wi-Fi signals. Based on the intuition that a specified activity introduces a unique pattern in the time-series of waveform for Channel State Information (CSI-waveform), Wi-Play leveragesthe unique pattern in the CSI-waveform values as the indicator of human activities. Wi-Play consists of two commercial Off-The-Shelf (COTS) WiFi devices, a TP-LINK TL-WR842N router as the transmitter and an Intel NUC D54250WYKH laptop as the receiver. As our major contributions, we propose a human activity detection algorithm for the extraction of CSI-waveform, build multi-classifiers to recognize human activities, and implement a realtime human activity recognition system using a series of novel technologies. Wi-Play achieves more than 88.3% average recognition accuracy for recognizing human activities.